| Entity Recognition (NER) Model Card | |
| Model Overview | |
| Model Name: NER LSTM Model | |
| Description: A LSTM-based model for Named Entity Recognition (NER) task. The model aims to classify words in text into their respective named entity categories such as Person, Organization, Location, etc. | |
| Intended Use | |
| Primary Use Case: Extracting named entities (e.g., names of people, organizations, locations) from text. | |
| Usage Instructions: | |
| Install the required libraries: Ensure that the required libraries, such as pandas, scikit-learn, keras, and tensorflow, are installed. | |
| Load the model and tokenizer: Use the Hugging Face Transformers library to load the model and tokenizer from the provided files. | |
| Tokenize input text: Preprocess input text and tokenize it using the loaded tokenizer. | |
| Make predictions: Feed the tokenized input through the model to obtain predictions for named entity categories. | |
| Post-process predictions: Use the LabelEncoder to map model predictions back to human-readable named entity categories. | |
| Performance and Evaluation | |
| Performance Metrics: | |
| Test Loss: The loss value achieved on the test dataset. | |
| Test Accuracy: The accuracy achieved on the test dataset. | |
| Training Accuracy: The accuracy achieved on the training dataset. | |
| Validation Accuracy: The accuracy achieved on the validation dataset. | |
| Performance Summary: | |
| The model achieved an accuracy of approximately [Test Accuracy] on the test dataset. | |
| Training and validation accuracies are provided for reference. | |
| Dataset | |
| Dataset Name: NER dataset.csv | |
| Description: The dataset contains labeled data for named entity recognition. It includes columns for 'Word' and 'POS' (Part-of-Speech) labels. | |
| Model Details | |
| Architecture: | |
| Embedding Layer: Converts input tokens into dense vectors. | |
| LSTM Layer: Processes the sequence of word embeddings. | |
| Dense Layer: Produces a probability distribution over named entity categories. | |
| Hyperparameters: | |
| Embedding Dimension: 100 | |
| LSTM Units: 128 | |
| Batch Size: 64 | |
| Max Sequence Length: 100 | |
| Optimizer: Adam | |
| Loss Function: Sparse Categorical Cross-Entropy |